The normal methods for monitoring environmental pollution with image data have many false positives. Therefore, this study is proposing a single-valued neutrosophic set (SVNS) (a variant of NS)-based method as a decision-making model using intuitionistic fuzzy Hamacher generalized Shapley Choquet integral operators for feature extraction and automatic material classification in mining area using satellite data. The experimental results show that this decision-making model using intuitionistic fuzzy Hamacher generalized Shapley Choquet integral operators for feature extraction and automatic material classification can better predict the presence of four heavy metals, i.e., vanadium(V), iron (Fe), copper (Cu), and nickel (Ni) in the study area than other methods. For vanadium metal, the determination accuracies, namely, producer accuracy, user accuracy, overall accuracy, and Kappa were 94.5%, 94.1%, 93.88%, and 0.93%, respectively. It was found that the estimated results and the distribution trend of heavy metals are almost the same as in actual ground measurements.

Industry 5.0 has a focus on human-centric technology and sustainable operations. The study examines how fuzzy AI fits within the goals of Industry 5.0. These goals are customization, sustainability, efficiency and human collaboration. The research shows that fuzzy systems can process unclear and uncertain information. They can identify which supply chain strategies are more suitable. This approach gives supply chain operations to improve. The results show that fuzzy AI enhances adaptability. It increases the ability of organizations to respond to modern supply chain challenges. This research explains the role of fuzzy AI in Industry 5.0. It shows that the technology can improve the accuracy of decisions. It also supports sustainable practices. It makes technology more human-centered. The study concludes with practical ideas on how fuzzy AI can be used in real supply chains. It gives suggestsions in the directions for future research and applications in Industry 5.0.

Fashion designers and brands use GANs to create new and unique patterns, styles, and textures. GANs consist of a generator and a discriminator, which work together to produce high-quality, realistic outputs. VAEs are another type of generative model that is applied to generate new fashion designs. VAEs are known for their ability to generate diverse outputs by sampling from a learned latent space. Fashion designers can use VAEs to explore different design variations and styles. StyleGAN and its successor, StyleGAN2, are advancements of GANs that specifically focus on generating high-resolution and realistic images with control over different style elements. These models have been employed in fashion to create detailed and visually appealing designs. These AI generative models have the potential to revolutionize the fashion industry by facilitating creativity and providing new avenues for artistic expression. However, it's essential to consider ethical implications, intellectual property rights, and the responsible use of AI technologies in the context of fashion design.

We developed a research on Privacy and Ethical Norms where we quantifies these values using Bipolar Fuzzy Sets to ensures that modern systems behaves according to human values. For any specific context, we defines a bipolar state where a positive membership represents the presence of an ethical value like trust or anonymization and a negative membership represents a counter-value such as sadness or breach risk. We calculates the positive function as the average rating divided by ten and we represents the negative function as the negative average rating divided by ten. For example, if we have an AI that manages data with a high anonymization score of 8 but a risk of 2, we obtains a bipolar state showing strong positive presence and low negative risk. We utilizes t-cuts to filter out con-texts that does not meet our rigorous ethical standards, ensuring that only states with robust privacy measures is allowed to proceed in the decision-making pipeline.

This study looks at how Fuzzy Artificial Intelligence (AI) can change the game by boosting decision-making in SCM under the Industry 5.0 vibe. Fuzzy AI is awesome because it can handle messy and complicated data, making it easier to grasp and predict how supply chains work. The findings indicate that Fuzzy AI increases SCM's ability to adapt and respond. This will be helping businesses to deal with the difficult nature of modern supply chains. When looking at it through the Industry 5.0 lens, this research highlights how Fuzzy AI can totally change SCM by improving decision-making accuracy, encouraging sustainability, and pushing human-centered tech innovations. The study wraps up by talking about the real-world effects of using Fuzzy AI in SCM and points out some cool directions for future research and implementation within the Industry 5.0 framework